CAT/strategy/BECAT_strategy.py

import numpy as np

from CAT.strategy.abstract_strategy import AbstractStrategy
from CAT.model import AbstractModel
from CAT.dataset import AdapTestDataset
import random

class BECATstrategy(AbstractStrategy):
    
    def __init__(self):
        super().__init__()

    @property
    def name(self):
        return 'BECAT Strategy'
    
    def adaptest_select(self, model: AbstractModel, adaptest_data: AdapTestDataset,S_set):
        assert hasattr(model, 'delta_q_S_t'), \
            'the models must implement delta_q_S_t method'
        assert hasattr(model, 'get_pred'), \
            'the models must implement get_pred method for accelerating'
        pred_all = model.get_pred(adaptest_data)
        selection = {}
        for sid in range(adaptest_data.num_students):
            tmplen = (len(S_set[sid]))
            untested_questions = np.array(list(adaptest_data.untested[sid]))
            sampled_elements = np.random.choice(untested_questions, tmplen + 5)
            untested_deltaq = [model.delta_q_S_t(qid, pred_all[sid],S_set[sid],sampled_elements) for qid in untested_questions]
            j = np.argmax(untested_deltaq)
            selection[sid] = untested_questions[j]
        # Question bank Q
        return selection
    